SHERLOCK — Neural Network Software for Automated Problem Solving

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Qiming Zhang

My final year project is a research on neural-symbolic systems. It contains one software, one test and two small researches. First, one software has been created to translate a general definite logic program to either a Tp-neural network or a CILP- neural network automatically. Both two types of neural-symbolic systems can perform deductive reasoning by massively parallel computing. Based on it, a research has been done on a knowledge refining system, since such neural networks are embedded with knowledge and can also be trained by the standard BP algorithm. In a trained neural network, the original logic knowledge is refined by inductive learning. Third, we tested the software on Einstein’s Riddle because the riddle is famous for its hardness. Finally, a research has been on a new type of neural networks which enables classical negation. In traditional logic programming, an atomic formula has two states ®C positive or default negative, which means if some knowledge does not exist in the data base, its negation will be thought as true. If people use the classical negation, people have to use extended logic programs which deploy two atoms to express the information. I proposed a new type of neural networks which enables classical negation. We all tested it using the Einstein°Øs Riddle.